Prior research on discriminative features for facial recognition showed that the most important regions of the face were generally located in and around the eyes and nose (Sec. REF ). Fig. REF  shows an example of this saliency map computed for a probe image of Taylor Swift using the VGG-16 [1]} network as the whitebox face matcher. Using this saliency map as our prior probability for generating random masks allows us to sample the space of most salient masks that will affect the loss more efficiently than assuming a uniform probability across the entire image. Further limiting this prior to the upper 50th percentile of the mean EBP effectively eliminates the possibility of masking out unimportant background elements.
